A position estimate, which may also be referred to as a position “fix”, for a mobile device, may be based at least in part on distances or ranges measured from the mobile station to one or more wireless transmitters in a wireless system. Such systems may include wireless communication systems including, for example, cellular communication systems or wireless local area networks, to name just a couple of example types of wireless communication systems. Wireless communication systems may employ one or more wireless transmitters/receivers that may be referred to as “base stations” or “access points,” for example. A position fix is traditionally reliant upon knowledge of positions of the one or more wireless transmitters in the system. Accuracy or availability of mobile device positioning systems may depend, at least in part, on wireless transmitter mapping, wherein information related to wireless transmitters including estimated locations may be stored in a positioning database.
The positioning database may contain various types of information, including, for example, information that may be used in position estimation operations. Positioning databases may contain information related to positioning via radio frequency (RF) signal measurements within a mapped environment. For example, various RF signal characteristics may be measured within an area and leveraged to create various signaling environment characteristic models (e.g., heatmaps). The heatmaps may be maintained and refined over time, and which may allow for location position determination by mobile devices within an indoor structure.
To develop a positioning database for use in mobile device positioning operations, a venue can collect data for the environment with trained system surveyors or operators. For example, a surveyor may physically map out WiFi access points (AP) within an interior of a building and associate measurements with coordinates in a map. Detailed maps of an environment may be required before mapping begins because real-time map generation and tracking may be too processor or bandwidth intensive. Additionally, information collected with traditional single person surveys may be expensive, slow, erroneous, outdated, or missing some AP location information. For example, an AP may be relocated from one location to another or be replaced with a different AP. AP location data collection may pose a significant obstacle to the expansion of mobile device location operations for indoor areas. After a survey of APs is performed, perhaps at a significant cost, the surveying process may need to be repeated on a regular basis to track changes and provide the most up to data AP location information. Such overhead and reliance on professional surveyors in maintaining or setting up a wireless positioning system may result relatively large costs in maintenances or result in unreliable or inaccurate positioning performance.
Alternatively, a group of devices working independently towards a similar goal and with occasional direction from a server (e.g., crowdsourcing) may obtain RF measurements for absolute mobile device positions in a map of an environment. However, crowdsourcing RF measurements for a map typically requires coordinates of the mobile device at specific/absolute positions within the map to be known, so coordinate may be associated with a respective RF signal measurement at that coordinate in the map. However, requiring known coordinates within a map (e.g., absolute position of the mobile device) for RF signal measurements limits the applicability of traditional crowdsourcing of RF measurements for areas of a map where mobile device positioning is often uncertain, such as deep indoors where satellite fixes are unavailable. Therefore, updated techniques for efficiently maintaining or updating a wireless positioning database can help improve the adoption and accuracy of positioning for indoor venues.